Self-supervised learning (SSL), also named representation learning, aims to learn general visual representation for downstream tasks.
Early works rely on ad-hoc heuristics to design pretext tasks [1]}, [2]}, [3]}, [4]}, which limits the generality of learned representations.
Recent approaches can be categorized as discriminative [5]}, [6]}, [7]}, [8]}, [9]} or generative [10]}, [11]}, [12]}, [13]}.
Contrastive methods [5]}, [6]}, [7]}, [8]} are representative for discriminative methods, which enforce a consistency loss between different views of the same image by contrastive positive pairs against negative pairs, has shown promising results recently.
We notice the unsupervised learned visual representation by SSL pre-training has strong discriminative power, and we exploit it for better instance mining.
